FBA_FML_AIforTrading Term1_Lesson 14 Quiz3.ipynb

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    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "def estimate_volatility(prices, l):\n",
    "    \"\"\"Create an exponential moving average model of the volatility of a stock\n",
    "    price, and return the most recent (last) volatility estimate.\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    prices : pandas.Series\n",
    "        A series of adjusted closing prices for a stock.\n",
    "        \n",
    "    l : float\n",
    "        The 'lambda' parameter of the exponential moving average model. Making\n",
    "        this value smaller will cause the model to weight older terms less \n",
    "        relative to more recent terms.\n",
    "        \n",
    "    Returns\n",
    "    -------\n",
    "    last_vol : float\n",
    "        The last element of your exponential moving averge volatility model series.\n",
    "    \n",
    "    \"\"\"\n",
    "    # TODO: Implement the exponential moving average volatility model and return the last value.\n",
    "    \n",
    "    log_return = np.log(prices / prices.shift(1))[1:]\n",
    "    reverse = log_return[-1::-1]\n",
    "    n = len(reverse) - 1\n",
    "    num = 0; denom = 0\n",
    "    for i in range(n + 1):\n",
    "        num += (l ** i) * reverse[i] ** 2\n",
    "        denom += l ** i\n",
    "    last_vol = np.sqrt(num / denom)\n",
    "    return last_vol\n",
    "        \n",
    "    return vol\n",
    "    \n",
    "def test_run(filename='data.csv'):\n",
    "    \"\"\"Test run get_most_volatile() with stock prices from a file.\"\"\"\n",
    "    prices = pd.read_csv(filename, parse_dates=['date'], index_col='date', squeeze=True)\n",
    "    print(\"Most recent volatility estimate: {:.6f}\".format(estimate_volatility(prices, 0.7)))\n",
    "\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    test_run()"
   ]
  }
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